iranian journal of economic studies vol. 4, no. 1, spring 2015,...

Iranian Journal of Economic Studies

Vol. 4, No. 1, Spring 2015, 101-126

Non-Linear Relationships Among Oil Price, Gold Price and Stock Market

Returns in Iran: A Multivariate Regime-Switching Approach

Siab Mamipour

Department of Economic, Kharazmi

University, Tehran,

[email protected]

Fereshteh Vaezi Jezeie

Department of Economic, Kharazmi

University, Tehran,

[email protected]

Abstract In this paper, the effects of oil and gold prices on stock market index are

investigated. We use a cointegrated vector autoregressive Markov-switching

model to examine the nonlinear properties of these three variables during the

period of January 2003 - December 2014. The Markov-switching vector-

equilibrium-correction model with three regimes representing "deep recession",

"mild recession" and "expansion" provides a good characterization of the sample

data. The results of the model show that the impact of oil price on stock returns

is positive and significant in the short run. However, it has negative effects on

stock market in the long run. Moreover, we find out that the relationship

between gold price and stock market returns varies during the period under

investigation depending on the market conditions. More specifically, the

positive gold price shock decreases the stock market returns in the short run (10

months), while it increases the stock market returns in the medium and long run.

Keywords: Stock Market Price, Oil Price, Gold Price, Markov Switching-

Vector Error Correction Model (MS-VECM), Iran.

JEL Classification: E32, E37, C32, G17.

Received: 14/10/2015 Accepted: 13/4/2016

The Corresponding Author

Iranian Journal of Economic Studies, 4(1), Spring 2015 102

1. Introduction

The stock market in economy is known as an organized and official

market of capital and its most important task is mobilization and

allocation of financial resources and turning them into capital. Achieving

the desired economic growth and development without long-run financial

resource mobilization is impossible. Yet, one of the main characteristics

of developing countries is that they suffer from fragmentation and

scarcity of savings and capital and the capital is not being conducted to

optimum routs of production. Thus, it is necessary to form a strong and

efficient capital markets in order to optimize the circulation of financial

resources and conduct them to optimum routs of economy (Dimson,

1988).

One of the important components of capital market is the stock

exchange. The existence philosophy of capital market and also stock

market is that they transfer funds from sector with surplus to funds from

sectors in need of cash. If the sectors invest the funds in producing the

capital, it is expected to be added to the wealth of society. Accordingly,

increase the returns of capital markets is the primary objective of market

policy makers and legislators (Barvch, 1988). It has been argued that the

most common method for making investment decisions in the stock

market is the study and the awareness of the stock price trend. Stock

price index shows the general trend of the stock market moves. In fact,

the degree of capital market success or failure is detected according to the

trend. Thus, portfolio managers and other natural and legal people who

deal with stock trading and other financial assets in the market need

various factors affecting the stock price under different economic

conditions, in order to maintain and increase the value of their portfolio.

Several factors affect the returns of the stock exchange. One of the

affecting factors is the oil price. Oil and its products are used as the main

sources of energy in manufacturing processes in the world; thus,

volatilities in the oil price can affect the cost of production and

profitability of production companies. Oil is the most important source of

income for some exporting countries and through this, its price and

volatilities can affect natural sector and also the capital market, so that in

many countries with poor management of oil revenues, oil price is

coupled with an increase in government revenue and monetary base and

has the inflation effects. Rising inflation will have a positive effect on the

stock price.

Non-Linear Relationships Among the Oil Price, the Gold Price … 103

Iran has 11 percent of world oil reserves and is the second largest

producer in the Organization of Petroleum Exporting Countries (OPEC).

Based on the time series data of the central bank of the Islamic republic

of Iran, 80 to 90 percent of total export revenue and 40 to 50 percent of

the annual government budget come from oil exports. In addition, the

sale of oil is over 20 percent of GDP of Iran; so Iran's economy is largely

dependent on exports of crude oil. Shocks in world oil markets can have

a great impact on the economic structure of Iran. Another factor affecting

the capital market is the price of gold. Gold is considered as the most

important world monetary standard which is mostly used in coins and

gold bullion as an international monetary reserve. Gold is also part of the

central bank's intemational reserves. From the economic point of view,

gold can be considered an strategic commodity.

Depression and expansion of stock exchange affects the national

economy. It is clear that depression and expansion of stock exchange

may be caused by many factors in the economy. As a powerful

exogenous variable, the world oil price would affect many

macroeconomic variables such as stock price index. On the other hand

the changes in the world gold price captures many international monetary

and financial development. Therefore, the main objective of this study is

to investigate the effect of the oil and the gold prices on the stock market

in Iran.

2. Literature Review

Several studies have been done on the relationships between different

markets (gold, oil and capital) which can be presented in different

categories. It has been tried to classify and present the studies based on

result analysis method, namely linear and nonlinear time-series methods.

All the studies done by Papapetrou (2001), Maghyereh (2004), Park

and Ratti (2008), Fayyad and Daly (2011) and Mohd Hussin and et al.

(2012) have used vector auto regression method (VAR) on the markets of

stock, oil and in some cases gold in different countries. The results have

been interpreted in the following.

Papapetrou (2001) showed that oil price affect real activities of

Greece’s economy and employment, while no evidence of relationship

between oil prices and stock returns can be found. Maghyereh (2004)

tested the dynamic relationship between crude oil price and stock returns


in twenty-two emerging market economies. She showed that oil price

shocks do not have significant impact on the stock index returns.

Park and Ratti (2008) studied the oil market price volatilities on key

financial variables such as stock prices in the United States and thirteen

developed countries. The results of their studies indicate that there is

statistically a significant relationship between oil price volatility and

stock index in the mentioned markets. However, type of the relationship

depends on countries whether they are exporter or importer and this is

evident with regard to the short time period of one month. Fayyad and

Daly (2011) compared the effect of oil price shocks on the stock returns

in GCC countries, America and the UK. They have concluded that the

predictive power of oil price in the stock market has been increased after

a rise in oil price during the global crisis. Qatar and the UAE among

GCC countries and the UK compared to other countries have been more

responsive to shocks.

Mohd Hussin and et al. (2012) studied the relationship between of

strategic goods (i.e oil and gold prices) and Malaysia's stock market. The

aim of their study was to investigate the dynamic effects of oil prices and

changes in gold prices on the stock market of Malaysia by using vector

auto regression (VAR). They, applying correlation analysis, Granger

causality test, Impulse Response Function (IRF) and Variance

decomposition (VDC), showed that the stock returns do not have

integrated correlation with strategic goods in the long run. According to

Granger causality, it was observed that the two-way causality relationship

is only between stock returns and oil price. So only oil price variables

among the strategic goods will affect stock returns in the short-run in

Malaysia. This implied that the gold price is not a valid variable to

predict the stock price changes.

The non-linear methods have been used in all the studies done by

Maghyereh and Al-Kandari (2007), Arouri and Fouquau (2009), Arouri

and Nguyen (2010), Nguyen and Bhatti (2012) and Mansoor Baig and et

al. (2013), on the markets of stock, oil and in some cases gold in different

countries. The results have been interpreted in the following.

Maghyereh and Al-Kandari (2007) studied the relationship between

oil price and stock markets in Arabic countries located around the Persian

Gulf and used the non-linear correlation analysis and error correction

mechanism in their studies. They showed that in the mentioned countries,

oil price would affect the index of stock prices in the long-run but the

http://www.sciencedirect.com/science/article/pii/S0301421510002806


relationship is non-linear. Arouri and Fouquau (2009) examined short-

run relationships between oil price and stock markets of Arabic countries

located around the Persian Gulf, by using nonparametric Kernel

regression method with the Gaussian Kernel. The result was that the

relationship between stock returns and oil price in Qatar, Oman and

United Arabic Emirates was nonlinear and there is asymmetry between

changes of oil price and stock returns in these countries. They did not

find a significant relationship in other Arabic countries in the Persian

Gulf region. Afterward in 2010, Arouri and et al. studied the stock

market responses of Arabic countries in the Persian Gulf region to

volatilities of the oil price. They showed that the stock market returns

significantly respond to oil price volatilities in Qatar, Oman, Saudi

Arabia and the United Arab Emirates. Moreover, the relationship

between the oil price and stock market in the countries is non-linear and

depend on the oil price. However, the significant relationships were not

seen between mentioned variables in Bahrain and Kuwait. Also Nguyena

and Bhatti (2012) studied the relationship between the oil price and stock

market in China and Vietnam by using copula functions and parametric

and nonparametric methods. They found that there was tail dependence

between oil world price and stock market index of Vietnam; however this

relationship had the opposite result for stock market of China. Finally,

Mansoor Baig and et al. (2013) studied the relationship between the oil

price, gold price and stock market returns of Karachi in Pakistan and

concluded that there was not a significant relationship between gold price

growth, oil price growth and stock market returns of Karachi in the long-

run.

3. Data

The monthly data index of this study is taken from Iran's stock market

and then transformed into the natural logarithms to examine the

economic relationship between the oil price (LnOil) and the gold price

(LnGold) effect on Stock market returns (LnStock). The sample size of

the study was 144 observations that were started from January 2003 until

December 2014. The study structure was a quantitative approach and

several steps were undergone before applied MS-VECM on examine the

economic relationship model.

Figure (1) shows the diagrams of logarithm values of the oil price

(OPEC basket oil (dollars)), gold price (World Gold ounces (dollars))


and the stock price (total index of stock exchange) in Iran. As it can be

seen, the diagram related to the total index of stock has volatility and

reached its peak in December 2013 and its minimum in March 2003.The

diagram related to the oil price has a lot of volatilities and these

volatilities are more tangible during July 2008 (peak) to December 2008.

The diagram related to the gold price approximately kept its upward trend

until September 2011 and has started to decline afterwards.

Figure 1: The Variables in Level

Figure (2) shows the diagrams of logarithm values of the stock price,

oil price and gold price after taking the first differencing process. The

diagram of the stock market returns shows a lot of volatilities, as the

maximum returns were in July 2003 and the minimum was in December

2008. Also the diagram related to the oil price shows the maximum

amount of changing in June 2009 and minimum in October 2008 and

finally the diagram related to the gold price shows the maximum positive

change in August 2011 and the minimum negative change in June 2006.


Figure 2: The Variables in First Differencing

4. Econometrics Method: MS-VECM

Various time-series models have been used to analyze the behavior of

economic and financial variables. Linear models such as Auto Regression

model (AR), Moving Averages (MA) and their combination (ARMA)

model as the common models were used. Although these linear models in

many cases are good fitting, they are not able to illustrate non-linear

dynamic patterns of variables due to their ability to detect asymmetry

(including structural break in time-series). Thus, non-linear models are

presented.

Recently, Markov Switching model (MS) has increasingly been used

in international studies and is one of the popular models of nonlinear

time-series. This model consists of multiple structures which can check

the behavior of the time-series of different regimes. A new form of

Markov Switching model is the conversion and transmission mechanisms

between different structures and regimes which are controlled by

invisible status variable which is following the Markov first order chain.

The main form of regime shift model is changing all or some of the

parameters based on Markov processes in different conditions or regimes.

Different situations are shown by invisible variable. The logic of this

kind of modeling is the combination of different distribution with

different characteristics. The current values of variables are extracted


from this model, according to the more probable state (invisible) which is

determined by observation.

The Markov switching model was introduced by Quandt (1972),

Goldfeld and Quandt (1973) for the first time and then, was developed

for the extraction of business cycles by Hamilton (1989). Unlike other

nonlinear methods such as Artificial Neural Network (ANN) and Smooth

Transition Autoregressive (STAR) in which the transition from one

regime to another is a gradual switching, sudden switching is done in the

Markov model.

Also vector auto regression method is one of the methods used in

studying the relationships among economic variables. One of the

disadvantages of the method is that it assumes all the variables

considered in the model are stationary, but in fact they are not. When all

variables or one of them are not stationary, vector error correction model

could be a good alternative for vector auto regression model. Vector error

correction model (VECM) relates the short-run volatilities of variables to

the long-run equilibrium values and considers short-run dynamic

response of variables. Vector error correction model is a model for

linking the short-run to long-run relationships based on VAR model with

the convergence characteristics.

When the series are non-stationary in levels and cointegrated, a

bivariate vector error correction model (VECM) of order p can be used to

jointly modeling rates behavior. Formally: 1

1

1

p

t i t i t t

i

y y y

(1)

Where ty =[LnStock LnOil LnGold]´ is a three-dimensional vector of

Stock, Oil and Gold Market; LnStock is the natural logarithm of total

price index differential as stock market return; LnOil represents the oil

price differential and LnGold is the gold price differential. i is 3×3

autoregressive short-run parameters matrices and is the long-run

impact matrix, whose rank r determines the number of cointegrating

vectors. In the bivariate case, can be partitioned into a 3×1 vector

of long-coefficients, representing the long-run relationships among the

variables, and a 3×1 vector α containing the equilibrium correction

(speed of adjustment) coefficients ( ).


Model (1) assumes that the relationships among stock market, gold

and oil price rates are symmetric and linear. Stock market return series

are characterized by occasional jumps or structural changes in their levels

or volatility. The presence of important discrete economic events induces

substantial non-linearity in the stochastic process and distorts inference if

it is not appropriately modeled. However, most of the studies adopt a

deterministic approach, which consists identifying structural breaks in the

series and modeling these shifts by augmenting the empirical

specification with an appropriate set of dummy variables or by

conducting split sample analyses (Blot and Labondance, 2011;

Panagopoulos and Spiliotis, 2012). When regime shifts are stochastic

rather than deterministic, these approaches may lead to biased or at least

inefficient results (Dahlquist and Gray, 2000; Clarida et al., 2006). In

such cases, a multivariate generalization of the univariate Markov-

switching (MS) model proposed by Hamilton(1989) represents a viable

alternative to allow for stochastic behavioural changes.

Multivariate generalized Markov switching model is introduced by

Krolzig (1996) as Markov Switching-Vector Error Correction Model

(MS-VECM) and is related to the concept of multiple equilibriums in

dynamic economic theory.

According to Krolzig study (1996), a Markov Switching-Vector Error

Correction Model (MS-VECM) in the form of equation (2) is a model

with regime dependent parameters:

1

1

1

( ) ( ) ( )p

t t t t i t t k t

k

y v s s x t s y u

(2)

The model is related to the concept of multiple equilibriums in

dynamic economic theory. From now on, every regime which is defined

by a system with the phrase ( )ts and long-run equilibrium ( )ts , would

be determined in the form of equation (3):

1

1

1

( ) ( ) ( )p

t t t t i t k t t

k

y s y s t y s u

(3)

In the model, it is assumed that a regime which is occurs in t time, is

invisible and depends on ( )ts an invisible process.

To complete the model, characteristics of ts process should be

identified. ts in the Markov switching model is considered as a first


degree Markov process. This assumption reflects the fact that ts only

depends on last period regime, means 1ts . In the following, we would

complete the model by introducing the equation (4) the transition

probabilities from one state to another one:

1 2 1 2 1Pr , ,...; , ,... Prt t t t t t t ijs j s i s k y y s j s i p (4)

Transition between regimes can be shown by transition probability

matrix. For example, the matrix is in the form of equation (5) for a two-

regime model:

1 1 11 12

1 1 21 22

Pr( 1 1) Pr( 2 1)

Pr( 1 2) Pr( 2 2)

t t t t

t t t t

s s s s p pP

s s s s p p

(5)

In equation (5), ( , 1,2)ijp i j shows the probability of transition from

ts j , assuming 1ts i and 1 2 1i ip p .

The MS-VECM model allows all the variables in it to be shocked, in

the context of the model. When the system is affected by the regime

transition, impulse response is checked by considering the transition of

variables of dependent on VAR feature regime and hidden Markov chain.

Impulse responses are often used in examining the relationship between

the dynamic model variables. So that the ultimate impact of impulse on

the system is being followed over time.

In Krolzig and Toro (1999) study, at first MS (M) -VECM(P-1) model

is considered in the form of equation (6) to analysis the impulse response

in Markov switching model:

1 1 1 1 1...t t t t p t p ty M y y y u (6)

In equation (6): 1:... :M M . MS (M)-VAR(P) model corresponded

to equation (6) is in the form of equation (7).

1 1t t t p t p ty M A y A y u (7)

In equation (7), 1j j jA and 1 1kA I for

1j j jA and 1< j < p are equal to 0p k . A show of MS(M)-

VAR(1) accumulated with a process of MS(M)-VAR(P) is used to

calculate the impulse response function. By considering

1( ,..., )t t t py y y equation (7) is rewritten as equation (8):


1t t t ty H JAy u (8)

The state-space representation is completed in the form of equation (9)

by the VAR(1) representation of the Markov chain (Hamilton, 1994):

1t t tF v (9)

In equation (9), t is the unobservable ( 1)M state vector consisting

of the indicator variables ( )tI S m for 1,...,m M ; Hence the

expectation of t hy conditional upon 1{ , , }t t tu y is given by equation

(10):

1t h t t h t t h ty H JAy

)10)

In equation (10), conditional expectation of t h is as equation (11):

(11)

According to equation (11), impulse response analyses (response of

system to normal shifts in variables) of linear VAR models are as

equation (12):

ht hj

jt

yJA

u

(12)

In equation (12), j is the jth column of the identity matrix. If

variance-covariance matrix ∑u is regime-dependent the standardized and

orthogonalized impulse-responses also become regime-dependent:

( )ht ht j

jt

yJA D

u

(13)

In equation (13), ( )t t tu D

and ( )tD

is a lower triangular matrix

resulting from Choleski decomposition of ( ) ( ) ( )t t tu D D

5. Empirical Results and Discussion

5.1. Unit Root Test At first, stationary of variables should be ensured to avoid spurious

regression. In this section, stationary of variables are checked by the

Augmented Dickey Fuller (ADF), Dickey Fuller GLS (ERS) and

Kwiatkowski-Philips-Schmidt-Shin (KPSS) tests. ADF and ERS tests

showed that under the null hypothesis of a unit root; their outputs

reported MacKinnon lower-tail critical and p-values for these tests, but


the KPSS test differed from the other unit root tests described here in that

the series are assumed to be stationary under the null hypothesis and the

KPSS output only provided the asymptotic critical values tabulated by

KPSS.

Test results, presented in Table 1, clearly indicate the presence of a

unit root for all the series in levels and a rejection for the series in first-

differences, providing evidence of an I(1) behavior.

Table 1: Unit Root Test

Ln(gold) Ln(oil) Ln(stock)

First diff. Level First diff. Level First diff. Level

a)Unit root test -10.6824

(0.000)

-1.6817

(0.4384) -7.5060

(0.000)

-2.0693

(0.2574)

-6.2123

(0.000)

-0.6374

(0.8574)* ADF

-10.7053

(0.000)

0.7368

(0.4624) -7.2494

(0.000)

-1.0174

(0.3107)

-6.0998

(0.000)

0.8738

(0.3837) DF-GLS

b) Stationary test

0.380 1.3077 0.1684 1.2106 0.1674 1.1160 KPSS** *The value in parentheses is represented to the p-value.

** Asymptotic critical values for the KPSS test are 0.739, 0.463 and 0.347 at the 1,

5 and 10% levels, respectively.

5.2. Cointegration Test

To estimate the cointegration model of Johansen, The optimal interval of

variables must be provided at first. Thus LR test and information

criterion are used to identify the optimal interval and also the optimal

number of regime and optimal model in Markov switching model. The

model is optimal when the amount of its information criterion is

minimum. Three of the most common information criterion includes:

The Akaike Information Criterion (AIC) Test:

2 2log( )AIC k L (14)

The Schwarz Information Criterion (SC) Test:

ln( ) 2 log( )SC k n L (15)

The Hannan-Quinn Information Criterion (HQ) Test:

2 ln(ln( )) 2 ln( )HQ k n l (16)

Where k represents the number of parameters, n is the number of

observations and L is the maximized likelihood value.


To indicate the optimal lag of VAR model and according to the table

(2), the number 8 is considered as the maximum of optimal interval in the

model. The optimal interval is 2, according to the existed criteria.

Table 2: VAR Lag Order Selection Criteria

Lag LogL LR FPE AIC SC HQ

0 -181.3006 NA 0.003017 2.710303 2.774552 2.736412

1 616.0590 1547.816 2.78e-08 -8.883220 -8.626221 -8.778782

2 650.5638 65.45768* 1.91e-08* -9.258291* -8.808543* -9.075525*

3 659.4575 16.47950 1.92e-08 -9.256728 -8.614231 -8.995633

4 667.0748 13.77844 1.96e-08 -9.236395 -8.401148 -8.896972

5 669.6355 4.518813 2.16e-08 -9.141699 -8.113703 -8.723947

6 678.255 14.83151 2.17e-08 -9.136111 -7.915366 -8.640031

7 685.1897 11.62494 2.25e-08 -9.105731 -7.692237 -8.531323

8 692.8311 12.47349 2.30e-08 -9.085752 -7.479508 -8.433015

* indicates lag order selected by the criterion, LR: sequential modified LR test

statistic (each test at 5% level), FPE: Final prediction error

At this point, we determine the number of cointegration vectors. This

is a means of testing for cointegration in a multivariate context, where

there is the possibility of more than one cointegrating vector being

present. It follows the same principles as the Engle-Granger approach to

cointegration, in so far as the order of integration of the variables are first

assessed, if the variables are I(1) the Johansen Maximum Likelihood

(ML) procedure can then be used to determine whether a stable long-run

relationship exists between the variables.

Johansen cointegration method based on ˆtrace , trace test and max̂ ,

maximum eigenvalue test is used to determine the number of

cointegration vectors. Summary of the results of maximum eigenvalue

test and trace test is in table (3). According to results of Table (3), all two

statistics of ˆtrace and max̂ confirm the existence of a cointegration

vector; because null hypothesis of no convergence vector is ruled out by

both statistics but the hypothesis of presence of more than one integration

vector is not ruled out.


Table 3: Johansen Test Outputs

Hypothesized

No. of CE(s) Trace statistics

Maximum

Eigenvalue statistics

ˆtrace

5% critical

value max̂ 5% critical

value

None* 41.2697*

(0.0098) 35.1927

25.6711*

(0.0163) 22.2996

At most 1 15.5985

(0.1940) 20.2618

12.1865

(0.1754) 15.8921

At most 1 3.4119

(0.5067) 9.1645

3.4119

(0.5067) 9.1645

The value in parentheses is represent the p-value

* denotes rejection of the hypothesis at the 0.05 level

Table 4 shows the convergent and normalized vector of the model. As

it is clear, the cointegration vector under consideration is normalized with

regard to variable of Lnstock.

Based on the results of linear error correction, stock price index has a

reverse relationship with the oil price and a direct relationship with the

gold price in long-run. Since, the variables used in this model are

logarithmic; we can interpret coefficients as elasticity. The long-run

elasticity of stock price index with regard to the oil price is -5.2399, this

means that one percent increase in the oil price decreases the stock price

index up to 5.2399 percent. Also, the long-run elasticity of the stock price

index with regard to the gold price is 4.5395 which means that one

percent increase in the gold price increases the stock price index up to

4.5395 percent in the long-run.

On the one hand, the oil price increases along with the increase in the

oil incomes which disrupts the allocation of financial sources in

government sector and will develop marginal investments and unfinished

projects; on the other hand, increase in importing consumer would

decrease the competition power of internal products and output of private

sector investments. As a result, those who work in private sectors are not

motivated to invest in exchangeable products sector and this issue affects

the capital market. The direct relation between the stock price and the

gold price has somewhat encountered difficulty in the long-run.

Considering such results, we should take necessary caution. Such

unexpected result may be due to stock market inefficiency and lack of


currency basket among people. Other reason of such results may be the

fact that the gold price in Iran is affected by two sides. On one hand, it is

affected by world gold price and on the other hand, it is affected by the

assessed economic policies.

Table 4: Cointegration Vector of Linear

CoinEq1 Cointegrating Eq:

1.0000 Lnstock(-1)

5.2399

(1.4500)

[3.6135] Lnoil(-1)

-4.5395

(1.2388)

[-3.6643] Lngold(-1)

-2.2744

(3.8802)

[-0.5861] C

The value in () is represent to the standard errors

The value in [ ] is represent to the t-statistics

5.3. MS-VECM Result

5.3.1. Testing for Non-Linear Relations and Regime Characteristics

Markov Switching models are created through change in mean, intercept,

heteroskedasticity and autoregression coefficients. Two conditions are

required for selecting an optimum model. Firstly, the null hypothesis

which is based on stable regime in the model must be rejected. Secondly,

the mentioned model must be more appropriate in terms of information

criteria than the probable models in which the first condition is proved.

Through investigating various techniques, taking into consideration

the nature of data and the optimum interval, we could identify three

regimes. Then, the models were compared based on AIC, SC, and HQ

criteria and LR test. At the end, MSIH(3)-VECM(1) model was selected

as the superior one. In this model, intercept and heteroskedasticity

variance depend on the regime.

Table 5 shows the results of estimating parameters of the above model

by using maximum likelihood method. The statistical amount of LR test

based on the linearity behavior of variables is 78.7059 and based on the


p-value number related to Davises Statistics, this hypothesis is rejected

and the non-linearity relation between these variables is confirmed.

Table 5: Criterion Test Results on the Markov Switching Models

MSIH(3) – VECM(1) Linear VECM

Log-likelihood 706.626 667.2740

AIC criterion -9.3187 -9.1025

HQ criterion -8.9380 -8.9248

SC criterion -8.3820 -8.6653

LR linearity test: 78.7059 DAVIES=[0.0000]

Chi(18) =[0.0000] Chi(24)=[0.0000]

The results of estimating MSIH(3)-VECM(1) model for investigating

the impact of the stock market return on the oil and the gold prices are

presented in Table 6. As it shows, during this study the impact of these

parameters can be divided into three regimes and also the variable

coefficients are statistically significant. Since, the intercept of regime 1 is

less than regimes 2 and 3, we can say that stock output in regime 1 is less

than the two other regimes. So, regime 1 is considered as deep recession,

regime 2 as less recession and regime 3 as expansion situation.

Coefficients of variables are significant. As it can be seen in Table 6,

the sign related to independent variable, oil price, is positive which

shows that there was a direct relation between the oil price and the stock

output in the short run. Moreover, gold price has negative effect on the

stock market returns.

CYN coefficient which shows the adjustment rate demonstrates that

for the total index of stock exchange in each period 0.6 percent is

adjusted which indicates the low rate of adjustment in the model.

The results in Figure (3) show that the three-regime MS-VECM model

is suitable for estimating the variables in the financial relationship model

because the fitted and 1-step predicted probabilities for all variables in

the system fit to the mean.


Table 6: MSIH(3)-VECM(1) Outputs

DLN(stock) t DLN(oil) t DLN(gold) t

Intercepts

Const (Regime 1)

Const (Regime 2)

Const (Regime 3)

-0.0523

(-6.4493)

-0.0077

(-1.5396)

0.0546

(5.9902)

-0.1248

(-4.0419)

0.0015

(0.1549)

0.0021

(0.0902)

-0.0179

(-1.0383)

0.0042

(0.7935)

0.0193

(1.5447)

Autoregressive coefficients

DLN(stock) t-1

DLN(oil) t-1

DLN(gold) t-1

0.3017

(4.5659)

0.0513

(1.2705)

-0.2335

(-3.5185)

0.0807

(0.5100)

0.1970

(2.0761)

-0.0004

(-0.0035)

-0.0557

(-0.6108)

0.0263

(0.5794)

0.1265

(1.4918)

Adjustment coefficient

CYN -0.006

(-2.7001)

-0.0124

(-2.6621)

-0.0013

(-0.5220)

Standard Error

SE (Regime 1)

SE (Regime 2)

SE (Regime 3)

0.0266

0.0241

0.0389

0.0922

0.0731

0.0400

0.0603

0.0336

0.0406 The value in () is represent to the t-statistics


Figure 3: MSIH(3)-VECM(1) Fit

Based on the results in Table 7, the dominant economical regime is

the second regime so that markets under investigation are in normal

conditions or recession (regime 2) with the probability of 0.5756 and the

average survival duration of 4.04, in prosperity duration (regime 3) with

the probability of 0.326 and the average survival duration of 2.38, and in

slump situation (regime 1) with the probability of 0.098 and the average

survival duration of 1.63.

Table 7: Regime Properties

No. of observations Probability Duration

Regime 1 14.3 0.0984 1.63

Regime 2 81.4 0.5756 4.04

Regime 3 46.4 0.3260 2.38 The situations of the three regimes are presented in Figure 4.


Figure 4: MSIH(3)-VECM(1) Probabilities Sketched

In order to investigate the degree of regimes instability and

probabilities of transfer of each regime to another, we have extracted

Matrix of transition probability. As Table 8 indicates, the probability of

transfer from regime one (deep recession) to regime 2 (less recession) is

0.61 and from regime 2 to regime 1 is 0.009. Probability values show that

regime 2 is more stable than regime 1. Also, the probability of transfer

from regime 1 to regime 3 (expansion) is very low and from regime 3 to

regime 1 is 0.168. As a result, regime 1 is more stable than regime 3. At

most, the probability of move from regime 2 to regime 3 is 0.237 and

from regime 3 to regime 2 is 0.251. So, the stability of regimes 2 and 3 is

almost the same.

In addition, the probability of transfer from regime 2 to regime 3 is

0.75 which is more stable than the other ones. The results obtained from

regimes transfer is in line with economic cycle’s theories and are

expected so that change of stagnation condition into prosperity condition

happens with less probability and in the long run, while change of

prosperity condition into stagnation condition happens with more

probability and in a short run.


Table 8: Matrix of Transition Probabilities

Regime 1 Regime 2 Regime 3

Regime 1 0.3860 0.6140 1.846e-006

Regime 2 0.0096 0.7526 0.2377

Regime 3 0.1683 0.2515 0.5803

5.3.2. Impulse Response Analysis

In next stage, the impact of shock on the particular variable on other

variables is investigated through Impulse Response Function (IR). The

analysis of active reciprocal impacts of created impulses in the system is

done through variance decomposition and Impulse Response Functions.

Impulse Response Functions (IRF) shows the active behavior of

pattern variables at the time of creating impulses on each of the pattern

variables in the duration. These impulses are usually selected as one

standard deviation. The origin or the beginning point of response variable

movement is the values related to the stable situation of system (without

impulse).

Figure 5 shows the response of the stock price index, the oil price and the

gold price toward one shock standard deviation to each variable in

different three regimes.

The response of stock market to the shock on the gold price in each of

the three regimes is similar and it is negative in short time, also it

decreases the stock price. But, gradually the effects disappear and after

about 10 months, the gold price shock would have a positive effect on the

stock market and it would have a positive and stable long term effect.

Moreover, the results show that the response of stock market to the

positive shock on the oil price in each of the situations decreases the

stock price. This effect is stable in all the short term and long term

durations.


Figure 5 : Impulse Response Analysis

6. Conclusion In this study, the impact of oil and gold prices on Iran's stock market

were investigated through monthly data covering the period January 2003

to December 2014. A cointegrated vector autoregressive Markov

switching model was used to examine the nonlinear properties of the

variables. Impact of shocks on each variable was investigated and was

anticipated.

Three different regimes including “deep recession”, “mild recession”

and “expansion” are used to characterize the sample data respectively.

The results obtained from the model show that the impact of the oil price

on stock return in each regime is statistically significant and positive in

the short run. However, it has negative effect on stock market in the long

run.

The relation between the gold price and the stock market return varies

during the period under investigation depending on the market

conditions. More specifically, the positive gold price shock decreases the

stock market returns in the short time (10 month), while it increases the

stock market return in the medium and long run. Our finding might have

implications for policymakers and investors in the stock market of Iran.


References Arouri, M. E. H., & Fouquau, J. (2009). On the short-term influence of

oil price changes on stock markets in GCC countries: linear and

nonlinear analyses, Cornell University Library.

Arouri, M. E. H., & Nguyen, D. K. (2010). Oil prices, stock markets and

portfolio investment: Evidence from sector analysis in Europe over

the last decade. Energy Policy, 38(8), 4528–4539.

Barvch, L. (1988). Toward a Theory of Equitable and Efficient

Accounting policy. The Accounting Review, 63, 15-37.

Blot, C., & Labondance, F. (2011). Bank interest rate pass-through in the

euro zone: monetary policy transmission during the boom and since

the financial crash. Paper presented at the 15th Annual Conference on

Macroeconomic Analysis and International Finance.

Clarida, R. H., Sarno, L., Taylor, M. P., & Valente, G. (2006). The role

of asymmetries and regime shifts in the term structure of interest rates.

Journal of Business, 79(3), 1193-1224.

Dahlquist, M., & Gray, S. F. (2000). Regimes switching and interest rates

in the European monetary system. Journal of International

Economics, 50, 399-419.

Dimson, E., & Mussavian, M. (1998). A brief history of market

efficiency. European Financial Management, 4, 91-193.

Engle, R F., & Granger, C.W. J. (1987). Cointegration and error

correction: representation, estimation and testing. Econometrica, 55,

251-276.

Fayyad, A., & Daly, K. (2011). The impact of oil price shocks on stock

market returns: comparing GCC countries with the UK and USA.

Emerging Markets Review, 12(1), 61-78.

Goldfeld, S. M., & Quandt, R. E. (1973). A Markov model for switching

regressions. Journal of Econometrics, 1(1), 3-15.

Hamilton J. D. (1989). A New Approach to the Economic Analysis of

Nonstationary Time Series and the Business Cycle. Econometrica, 57,

357-384.

Hamilton, J. D. (1994). Time series analysis (Vol. 2). Princeton:

Princeton university press.

Krolzig, H. M. (1996). Statistical analysis of cointegrated VAR processes

with Markovian regime shifts. Berlin: Humboldt University Press.

http://www.sciencedirect.com/science/article/pii/S0301421510002806

http://www.sciencedirect.com/science/journal/03014215

http://www.sciencedirect.com/science/journal/03014215/38/8


Krolzig, H. M., & Toro, J. (1999). A new approach to the analysis of

shocks and the cycle in a model of output and employment. European

University Institute. Working paper ECO, no. 99-30.

Maghyereh, A. (2004). Oil price shocks and emerging stock market: a

generalized VAR approach. International Journal of Applied

Econometrics and Quantitative Studies, 1(2), 27-40.

Maghyereh, A., & Al-Kandari, A. (2007). Oil prices and stock markets in

GCC countries: new evidence from nonlinear cointegration analysis.

Managerial Finance, 33(7), 449-460.

Mansoor Baig, M., Shahbaz, M., Imran, M., Jabbar, M., & Ain, Q.U.

(2013). Long run relationship between gold prices, oil prices and

Karachi stock market. Acta Universitatis Danubius, 9(5), 28-39.

Mohd Hussin, M. Y., Muhammad, F., Abu, M. F., & Awang, S. A.

(2012). Macroeconomic Variables and Malaysia Islamic Stock

Market: A Time Series Analysis. Journal of Business Studies

Quarterly, 3(4), 1-13.

Mohd Hussin, M. Y., Muhammad, F., Abu-Hussin, M. F., & Abdul

Razak, A. (2012). The Relationship between Oil Price, Exchange Rate

and Islamic Stock Market in Malaysia. Research Journal of Finance

and Accounting, 3(5), 83-92.

Nguyen, C.C., & Bhatti, M.I. (2012). Copula model dependency between

oil prices and stock markets: Evidence from China and Vietnam.

Journal of International Financial Markets, Institutions & Money, 22,

758–773.

Panagopoulos, Y., & Spiliotis, A. (2012). Is the Eurozone homogeneous

and symmetric? An interest rate pass-through approach before and

during the recent financial crisis. Paper presented at the 15th Annual

Conference on Macroeconomic Analysis and International Finance.

Park, J., & Ratti, R. A. (2008). Oil price shocks and stock markets in the

U.S. and 13 European countries, Energy Economics, 30(5), 2587-

2608.

Papapetrou, E. (2001). Oil price shocks, stock market, economic activity

and employment in Greece. Energy Economics, 23(5), 511-532.

Quandt, R. E. (1972). A New Approach to Estimating Switching

Regressions. Journal of the American Statistical Association, 67(338),

306-310.

http://www.emeraldinsight.com/loi/mf

http://www.emeraldinsight.com/toc/mf/33/7





http://www.tandfonline.com/loi/uasa20?open=67#vol_67

http://www.tandfonline.com/loi/uasa20?open=67#vol_67


Appendix:

A1: Regime Classification

Regime 1 Regime 2 Regime 3

2003:3 - 2003:3 [0.9756]

2003:9 - 2003:9 [0.9990]

2004:2 - 2004:2 [0.6931]

2006:6 - 2006:6 [0.9874]

2008:8 - 2008:12

[0.9996]

2009:12 - 2009:12

[0.9029]

2011:5 - 2011:5 [0.9693]

2011:10 - 2011:11

[0.7666]

2014:12 - 2014:12

[1.0000]

2003:4 - 2003:4 [0.9999]

2003:10 - 2003:10

[0.9979]

2004:3 - 2004:6 [0.9544]

2004:8 - 2005:11

[0.9528]

2006:1 - 2006:4 [0.9546]

2006:7 - 2007:8 [0.9118]

2007:11 - 2007:12

[0.9547]

2008:2 - 2008:5 [0.8729]

2009:1 - 2009:7 [0.9363]

2010:1 - 2010:2 [0.9769]

2010:5 - 2010:6 [0.8638]

2010:10 - 2010:12

[0.8245]

2011:6 - 2011:7 [0.8996]

2011:12 - 2012:8

[0.8976]

2013:1 - 2013:3 [0.8131]

2014:1 - 2014:11

[0.8463]

2003:5 - 2003:8

[0.9993]

2003:11 - 2004:1

[0.9321]

2004:7 - 2004:7

[0.9884]

2005:12 -

2005:12 [0.9806]

2006:5 - 2006:5

[0.9700]

2007:9 - 2007:10

[0.8875]

2008:1 - 2008:1

[0.9957]

2008:6 - 2008:7

[0.9999]

2009:8 - 2009:11

[0.8971]

2010:3 - 2010:4

[0.8850]

2010:7 - 2010:9

[0.9069]

2011:1 - 2011:4

[0.9037]

2011:8 - 2011:9

[0.9565]

2012:9 - 2012:12

[0.8980]

2013:4 - 2013:12

[0.9153]


A2: MSIH(3)-VECM(1) Diagrams


A3: MSIH(3)-VECM(1) Residuals

A4: MSIH(3)-VECM(1) Regime Shifts

iranian journal of economic studies vol. 4, no. 1, spring 2015,...

Documents